Min Kyung Lee
Carin Håkansta (Karolinska Institutet)
Karolinska Institutet
07/01/2023 to 12/31/2025
The School of Information allocation from this collaborative award is $28,381.
ALGOSH: Algorithmic management at work - challenges, opportunities, and strategies for occupational safety and health and wellbeing
Algorithms are at the forefront of a transformative shift in the World of Work, profoundly influencing work dynamics, organizational structures, and the work environment. Despite their profound impact, a substantial knowledge gap exists concerning algorithmic management (AM) and its repercussions on occupational safety, health, and wellbeing. This gap is particularly pronounced in non-platform work settings, where AM's prevalence is growing.
As the use of AM continues to expand across various economic sectors, it is imperative to investigate its effects on the wellbeing of workers. The overarching objective of the ALGOSH research program is to enhance our understanding of AM in non-platform sectors and its impact on the health, safety, and wellbeing of workers. Moreover, it aims to develop tools and strategies to mitigate associated risks. The three research aims of ALGOSH are:
- Facilitating the development of a standard for measurement of algorithmic management at work and related risks for health, safety and well-being.
- Increasing knowledge about the effects of algorithmic management on workers’ health, safety, and well-being.
- Investigating the balance of interests related to the control of algorithms in different legal contexts regarding occupational health and safety (OSH).
To accomplish this mission, an international and interdisciplinary consortium of researchers has been assembled. For our research to have maximum societal impact, the program also has a strong stakeholder involvement and support from trade unions, business organizations, international bodies, and government agencies. Their collective efforts will examine, discuss, and assess the opportunities and challenges posed by algorithmic management, fostering a safer and healthier work environment for all. The program applies multiple methods including quantitative, qualitative, literature reviews and participatory research.
Soo Young Rieh
Kenneth Fleischmann and R. David Lankes
Institute of Museum & Library Services (IMLS)
08/01/2022 to 07/31/2025
The award is $623,501 over the project period.
Training Future Faculty in Library, AI, and Data Driven Education and Research (LADDER)
The University of Texas at Austin School of Information will collaborate with librarians from Austin Public Library, Navarro High School Library, and the University of Texas Libraries to educate and mentor the next generation of Library and Information Science (LIS) faculty with expertise in artificial intelligence (AI) and data science. The Training Future Faculty in Library, AI, and Data Driven Education and Research (LADDER) program will apply a new Library Rotation Model to train doctoral student fellows to apply their AI and data science skills to conduct research in collaboration with librarians in distinct library settings. The project will increase the capacity of LIS programs to educate the librarians of tomorrow by preparing cohorts of outstanding future faculty who understand both cutting-edge IT and the unique service environment of libraries.
Ying Ding
Yan Leng, and Samuel Craig Watkins, University of Texas at Austin;
Yifan Peng Weill Cornell Medicine
AIM-AHEAD and National Institutes of Health (NIH)
09/17/2023 to 09/16/2025
The collaborative award is $998,739 over the project period. The School of Information portion of the award is $698,739.
Closing the loop with an automatic referral population and summarization system
Suicide is a public health concern and is ranked as the second leading cause of death in 10-24 years old.1,2 In particular, the increasing rates of suicide mortality and suicidal ideations and behaviors among Black youth in the United States (US) have become a pressing concern in recent years.3 Between 2001 and 2015, Black children under 13 years old were twice as likely to die by suicide, compared to their White counterparts.4 Furthermore, suicide mortality rates among Black youth have risen more rapidly than in any other racial or ethnic group.2,5 However, there remains a significant knowledge gap in understanding culturally tailored suicide prevention strategies for this population, particularly regarding unique social risk factors specific to Black youth. Specifically, a detailed understanding of social risk factors unique to Black youth and their differentiation from risk factors for other racial and ethnic groups is limited.2 This knowledge gap is critical, as research has indicated that Black youth face greater exposure to adverse childhood experiences (ACEs), which are linked to higher risks of suicidal ideation and attempts.
The National Violent Death Reporting System (NVDRS) is a state-based violent death reporting system in the U.S. that helps provide information and context on when, where, and how violent deaths occur and who is affected.11 However, much of the information in NVDRS is unstructured, limiting its use in examining a complete picture of the social risk factors contributing to Black youth suicide. Therefore, it is imperative to develop machine learning (e.g., natural language processing [NLP]) algorithms to automatically extract social risk factors from free text to help analyze Black youth suicide. Our long-term goal is to reduce the suicide rate by developing novel interventions targeting risk and protective factors among Black youth. The overall objective of this application is to develop and validate new AI approaches to identify individual-level social risks of Black youth suicide and enhance trust within the underserved communities regarding the approaches of AI/ML.
Kayla Booth
The Andrew W. Mellon Foundation
11/01/2021 to 10/31/2024
The award is $700,772 over the project period.
Summer Institutes for Advanced Study in the Information Sciences
The iSchool Inclusion Institute (i3) is an undergraduate research and leadership development program that prepares students from underrepresented populations for graduate study and careers in the information sciences. Only 25 students from across the country are selected each year to become i3 Scholars. Those students undertake a yearlong experience that includes two summer institutes hosted by the University of Texas at Austin’s iSchool and a research project spanning the year. i3 prepares students for the rigors of graduate study and serves as a pipeline for i3 Scholars into internationally recognized information schools—the iSchools. Most importantly, i3 empowers students to create change and make an impact on the people around them.
Ying Ding
National Science Foundation (NSF)
04/01/2023 to 03/31/2026
The award is $299,862 over the project period.
NSF-CSIRO: RESILIENCE: Graph Representation Learning for Fair Teaming in Crisis Response
The recent COVID-19 pandemic has revealed the fragility of humankind. In our highly connected world, infectious disease can swiftly transform into worldwide epidemics. A plague can rewrite history and science can limit the damage. The significance of teamwork in science has been extensively studied in the science of science literature using transdisciplinary studies to analyze the mechanisms underlying broad scientific activities. How can scientific communities rapidly form teams to best respond to pandemic crises? Artificial intelligence (AI) models have been proposed to recommend scientific collaboration, especially for those with complementary knowledge or skills. But issues related to fairness in teaming, especially how to balance group fairness and individual fairness remain challenging. Thus, developing fair AI models for recommending teams is critical for an equal and inclusive working environment. Such a need could be pivotal in the next pandemic crisis. This project will develop a decision support system to strengthen the US-Australia public health response to infectious disease outbreak. The system will help to rapidly form global scientific teams with fair teaming solutions for infectious disease control, diagnosis, and treatment. The project will include participation of underrepresented groups (Indigenous Australians and Hispanic Americans) and will provide fair teaming solutions in broad working and recruiting scenarios.
This project aims to understand how scientific communities have responded to historical pandemic crises and how to best respond in the future to provide fair teaming solutions for new infectious disease crises. The project will develop a set of graph representation learning methods for fair teaming recommendation in crisis response through: 1) biomedical knowledge graph construction and learning, with novel models for emerging bio-entity extraction, relationship discovery, and fair graph representation learning for sensitive demographical attributes; 2) the recognition of fairness and the determinant of team success, with a subgraph contrastive learning-based prediction model for identifying core team units and considering trade-offs between fairness and team performance; and 3) learning to recommend fairly, with a measurement of graph-based maximum mean discrepancy, a meta learning method for fair graph representation learning, and a reinforcement learning-based search method for fair teaming recommendation. The project will support cross-disciplinary curriculum development by effectively bridging gaps in responsible AI and team science, fair project management, and risk management in science.